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Across history, the most enduring lessons have not been absorbed through passive reading but through doing, tinkering, and experimenting. Whether it’s learning to cook, weave, engineer, or even getting enlightened, active participation (simply “doing”) shapes not just skills, but cognition itself. AI is no exception.
By integrating AI maker labs into K-12 education, we would not only teach students how AI works, but also foster curiosity, problem-solving skills, and the creative thinking needed to shape AI applications in the future. The question is: What changes when we learn AI by doing rather than just reading about it?
Take environmental science education. A textbook might introduce the concept of recyclable versus non-recyclable materials in a three-hour lesson. But imagine an AI maker lab project where students train an AI model to classify objects based on recyclability. They build a mechanised conveyor belt where a camera recognises items and diverts them into the appropriate bin. This simple system mimics real-world AI applications, teaching students not only how AI classifies objects but why AI gets things wrong, how bias enters AI models, and what ethical decisions are involved. Instead of a passive lesson, this process engages students in deep inquiry, raising questions about material life cycles, the economics of waste, and how AI can be applied to environmental sustainability.
This activity takes weeks instead of hours, but it leads to richer learning. Students don’t just memorise concepts; they question, experiment, create new ideas. It can encourage them to ask questions: Why is an item not recyclable? How can AI improve recycling systems at a national scale? Can we invent materials that are universally recyclable?
The shift is profound — from passive learning to engaged, inquisitive problem-solving. This approach does two critical things. One, it makes AI tangible. Rather than viewing it as an abstract black box, students see AI’s mechanics, biases, and limitations first-hand. Two, it prepares students for AI-driven careers. Students who learn by tinkering don’t just understand AI — they can apply it creatively and fearlessly to solve real-world challenges.
India has already made significant investments in makerspace infrastructure, particularly through initiatives like the Atal Tinkering Labs (ATLs) under the Atal Innovation Mission (AIM). With over 10,000 labs operational and plans for thousands more, this network offers a strong starting point to integrate AI literacy through making. But as anyone who has worked closely with these labs knows, infrastructure alone is not enough.
In practice, many labs struggle to fulfil their potential. Equipment lies unused, facilitators are under-trained, and the intended culture of experimentation is difficult to cultivate in resource-constrained environments. The idea of a makerspace is powerful. However, building a sustained practice of making, tinkering, and inquiry in schools is a long-term effort. It requires consistent mentorship, teacher support, and above all, capacity building.
Integrating AI maker labs into India’s education system must go beyond a top-down deployment of hardware or one-off training modules. With a technology that has challenged the world to think about how we will work and live, interventions must include a serious investment in teacher capacity, school leadership, and local ecosystems . This is a mindset shift — and it cannot be achieved overnight.
Moreover, these efforts must reach beyond elite private institutions. Enhancing ATLs with AI maker lab capabilities is, therefore, a practical idea. Over 60 per cent of labs today are — and the 50,000 newly announced ATLs are going to be — in government schools. Equitable access, especially in rural and government schools, requires strategic partnerships with state governments, teacher training institutes, and community organisations.
Despite challenges, the opportunity is too important to ignore. With thoughtful implementation, AI maker labs can bridge the gap between AI as a distant technology and as a lived experience for young Indians. They can help build a generation that doesn’t just consume AI, but questions it, builds with it, and adapts it to solve India’s unique challenges.
The road ahead is not easy. It will take time, funding, experimentation, and patience. But the outcome — a future where every child can understand and shape the technologies that define their world — is well worth the effort.
Vishwanath is the co-founder of MakerGhat & Inspirit, and a visiting research scholar at Stanford University. Vaishnav is a sociologist-technologist, and former mission director for the NITI Aayog Atal Innovation Mission. This article is the third of a series on AI In India
